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Multiomics Analysis Reveals the Prognostic Non-tumor Cell Landscape in Glioblastoma Niches.

ABSTRACT: A comprehensive characterization of non-tumor cells in the niches of primary glioblastoma is not fully established yet. This study aims to present an overview of non-malignant cells in the complex microenvironment of glioblastoma with detailed characterizations of their prognostic effects. We curate 540 gene signatures covering a total of 64 non-tumor cell types. Cell type-specific expression patterns are interrogated by normalized enrichment score across four large gene expression profiling cohorts of glioblastoma with a total number of 967 cases. The glioblastoma multiforms (GBMs) in each cohort are hierarchically clustered into negative or positive immune response classes with significantly different overall survival. Our results show that astrocytes, macrophages, monocytes, NKTs, and MSC are risk factors, while CD8 T cells, CD8 naive T cells, and plasma cells are protective factors. Moreover, we find that the immune system and organogenesis are uniformly enriched in negative immune response clusters, in contrast to the enrichment of nervous system in positive immune response clusters. Mesenchymal differentiation is also observed in the negative immune response clusters. High enrichment status of macrophages in negative immune response clusters is independently validated by analyzing scRNA-seq data from eight high-grade gliomas, revealing that negative immune response samples comprised 46.63 to 55.12% of macrophages, whereas positive immune response samples comprised only 1.70 to 8.12%, with IHC staining of samples from six short-term and six long-term survivors of GBMs confirming the results.

SUBMITTER: Xiao Z

PROVIDER: S-EPMC8481948 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Glioblastoma (GBM) displays marked cellular and metabolic heterogeneity that varies among cellular microenvironments within a tumor. Metabolic targeting has long been advocated as a therapy against many tumors including GBM, but how lipid metabolism is altered to suit different microenvironmental conditions and whether cancer stem cells (CSCs) have altered lipid metabolism are outstanding questions in the field. We interrogated gene expression in separate microenvironments of GBM organoid models that mimic the transition between nutrient-rich and nutrient-poor pseudopalisading/perinecrotic tumor zones using spatial-capture RNA-sequencing. We revealed a striking difference in lipid processing gene expression and total lipid content between diverse cell populations from the same patient, with lipid enrichment in hypoxic organoid cores and also in perinecrotic and pseudopalisading regions of primary patient tumors. This was accompanied by regionally restricted upregulation of hypoxia-inducible lipid droplet-associated (HILPDA) gene expression in organoid cores and pseudopalisading regions of clinical GBM specimens, but not lower-grade brain tumors. CSCs have low lipid droplet accumulation compared to non-CSCs in organoid models and xenograft tumors, and prospectively sorted lipid-low GBM cells are functionally enriched for stem cell activity. Targeted lipidomic analysis of multiple patient-derived models revealed a significant shift in lipid metabolism between GBM CSCs and non-CSCs, suggesting that lipid levels may not be simply a product of the microenvironment but also may be a reflection of cellular state. CSCs had decreased levels of major classes of neutral lipids compared to non-CSCs, but had significantly increased polyunsaturated fatty acid production due to high fatty acid desaturase (FADS1/2) expression which was essential to maintain CSC viability and self-renewal. Our data demonstrate spatially and hierarchically distinct lipid metabolism phenotypes occur clinically in the majority of patients, can be recapitulated in laboratory models, and may represent therapeutic targets for GBM.

Project description:BackgroundGlioblastoma (GBM) is a fatal brain tumor with no effective treatment. The specific GBM tumor immune microenvironment (TIME) may contribute to resistance to immunotherapy, a tumor therapy with great potential. Thus, an in-depth understanding of the characteristics of tumor-infiltrating immune cells is essential for exploring biomarkers in GBM pathogenesis and immunotherapy.MethodsWe estimated the relative abundances of 25 immune cell types in 796 GBM samples using single sample gene set enrichment analysis (ssGSEA). Unsupervised clustering was used to identify different GBM-associated TIME immune cell infiltration (GTMEI) patterns. The GTMEIscore system was constructed with principal component analysis (PCA) to determine the immune infiltration pattern of individual tumors.ResultsWe revealed three distinct GTMEI patterns with different clinical outcomes and modulated biological pathways. We developed a scoring system (GTMEIscore) to determine the immune infiltration pattern of individual tumors. We comprehensively analyzed the genomic characteristics, molecular subtypes and clinicopathological features as well as proteomic, phosphoproteomic, acetylomic, lipidomic and metabolomic properties associated with the GTMEIscore and revealed many novel dysregulated pathways and precise targets in GBM. Moreover, the GTMEIscore accurately quantified the immune status of many other cancer types. Clinically, the GTMEIscore was found to have significant potential therapeutic value for chemotherapy/radiotherapy, immune checkpoint inhibitor (ICI) therapy and targeted therapy.ConclusionsFor the first time, we employed a multilevel and multiplatform strategy to construct a multidimensional molecular map of tumors with different immune infiltration patterns. These results may provide theoretical basises for identifying more effective predictive biomarkers and developing more effective drug combination strategies or novel immunotherapeutic agents for GBM.

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Multiomics Analysis Reveals the Prognostic Non-tumor Cell Landscape in Glioblastoma Niches.

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